构建PubMed知识图谱
Building a PubMed knowledge graph
摘要：
PubMed是医学领域的重要资源，但有用的概念要么难以提取，要么含糊不清，这大大阻碍了知识发现。为了解决这个问题，我们构建了一个PubMed知识图谱（PKG），从2900万PubMed摘要中提取生物实体，消除作者姓名的歧义，通过美国国立卫生研究院（NIH）ExPORTER整合资金数据，从ORCID收集作者的从属关系历史和教育背景，并从MapAffil中识别细粒度的从属关系数据。通过整合可信的多源数据，我们可以在生物实体、作者、文章、隶属关系和资金之间建立联系。数据验证显示，生物实体提取的BioBERT深度学习方法在F1得分上明显优于最先进的模型（0.51%），作者姓名消歧（AND）达到了98.09%的F1得分。PKG可以引发更广泛的创新，不仅使我们能够衡量学术影响，知识使用和知识转移，而且还有助于我们根据作者和组织与生物实体的联系来分析他们。PKG可在Figshare（https：//figshare.com/s/6327a55355fc2c99f3a2，不包括PubMed原始数据的简化版）和TACC网站（http：//er.tacc.utexas.edu/pedets/ped，完整版）上免费获得。
英文摘要：
PubMed is an essential resource for the medical domain, but useful concepts are either difficult to extract or are ambiguated, which has significantly hindered knowledge discovery. To address this issue, we constructed a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER, collecting affiliation history and educational background of authors from ORCID, and identifying fine-grained affiliation data from MapAffil. Through the integration of the credible multi-source data, we could create connections among the bio-entities, authors, articles, affiliations, and funding. Data validation revealed that the BioBERT deep learning method of bio-entity extraction significantly outperformed the state-of-the-art models based on the F1 score (by 0.51%), with the author name disambiguation (AND) achieving a F1 score of 98.09%. PKG can trigger broader innovations, not only enabling us to measure scholarly impact, knowledge usage, and knowledge transfer, but also assisting us in profiling authors and organizations based on their connections with bio-entities. The PKG is freely available on Figshare (https://figshare.com/s/6327a55355fc2c99f3a2, simplified version that exclude PubMed raw data) and TACC website (http://er.tacc.utexas.edu/datasets/ped, full version).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
